## Abstract

[Extract]

The Editor has raised a very timely and interesting debate on

inductive and deductive reasoning. However, I am slightly deviating

from this issue, and I will rather concentrate on a seminal paper by

Leo Breiman (2001) on “Statistical Modelling: The Two Cultures”

and later on I will try to link it to this topic. According to Breiman,

“There are two cultures in the use of statistical modelling to reach

conclusions from data. One assumes that the data are generated

by a given stochastic data model. The other uses algorithmic

models and treats the data mechanism as unknown”. In the first

case like regression models, logistic regression, and Cox-model,

the values of the parameters are estimated from the data and the

models are used for information and/or prediction. The second

case, which Breiman calls “The Algorithmic Modelling Culture”, the

analysis in this culture is complex and unknown. Their approach

is to find a function f(x) through an algorithm that operates on

X to predict Y and Breiman himself called this a black box. The

machine learning algorithms like Decision Tree, Random Forest,

and Stochastic Gradient Boosting and to some extent ANN falls

in this category.

The Editor has raised a very timely and interesting debate on

inductive and deductive reasoning. However, I am slightly deviating

from this issue, and I will rather concentrate on a seminal paper by

Leo Breiman (2001) on “Statistical Modelling: The Two Cultures”

and later on I will try to link it to this topic. According to Breiman,

“There are two cultures in the use of statistical modelling to reach

conclusions from data. One assumes that the data are generated

by a given stochastic data model. The other uses algorithmic

models and treats the data mechanism as unknown”. In the first

case like regression models, logistic regression, and Cox-model,

the values of the parameters are estimated from the data and the

models are used for information and/or prediction. The second

case, which Breiman calls “The Algorithmic Modelling Culture”, the

analysis in this culture is complex and unknown. Their approach

is to find a function f(x) through an algorithm that operates on

X to predict Y and Breiman himself called this a black box. The

machine learning algorithms like Decision Tree, Random Forest,

and Stochastic Gradient Boosting and to some extent ANN falls

in this category.

Original language | English |
---|---|

Pages (from-to) | 3 |

Number of pages | 1 |

Journal | Biometric Bulletin |

Volume | 37 |

Issue number | 2 |

Publication status | Published - 2020 |